OpenRaiser/Intern-Atlas huggingface dataset:
Noise Models in Feature-based Stereo Visual Odometry
Venue: arXiv cs.RO 2016
Year: 2016

Feature-based visual structure and motion reconstruction pipelines, common in visual odometry and large-scale reconstruction from photos, use the location of corresponding features in different images to determine the 3D structure of the scene, as well as the camera parameters associated with each image. The noise model, which defines the likelihood of the location of each feature in each image, is a key factor in the accuracy of such pipelines, alongside optimization strategy. Many different noise models have been proposed in the literature; in this paper we investigate the performance of several. We evaluate these models specifically w.r.t. stereo visual odometry, as this task is both simple (camera intrinsics are constant and known; geometry can be initialized reliably) and has datasets with ground truth readily available (KITTI Odometry and New Tsukuba Stereo Dataset). Our evaluation shows that noise models which are more adaptable to the varying nature of noise generally perform better.
Proactive Location-Based Scheduling of Delay-Constrained Traffic Over Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.
A note on some inequalities used in channel polarization and polar coding
Venue: arXiv cs.IT 2016
Year: 2016

We give a unified treatment of some inequalities that are used in the proofs of channel polarization theorems involving a binary-input discrete memoryless channel.
Using Social Media to Promote STEM Education: Matching College Students with Role Models
Venue: arXiv cs.CY 2016
Year: 2016

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.
Domain Adaptation for Neural Networks by Parameter Augmentation
Venue: arXiv cs.CL 2016
Year: 2016

We propose a simple domain adaptation method for neural networks in a supervised setting. Supervised domain adaptation is a way of improving the generalization performance on the target domain by using the source domain dataset, assuming that both of the datasets are labeled. Recently, recurrent neural networks have been shown to be successful on a variety of NLP tasks such as caption generation; however, the existing domain adaptation techniques are limited to (1) tune the model parameters by the target dataset after the training by the source dataset, or (2) design the network to have dual output, one for the source domain and the other for the target domain. Reformulating the idea of the domain adaptation technique proposed by Daume (2007), we propose a simple domain adaptation method, which can be applied to neural networks trained with a cross-entropy loss. On captioning datasets, we show performance improvements over other domain adaptation methods.
From Migration Corridors to Clusters: The Value of Google+ Data for Migration Studies
Venue: arXiv cs.SI 2016
Year: 2016

Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about "places lived" from millions of Google+ users in order to study migration "clusters", i.e. groups of countries in which individuals have lived. For the first time, we consider information about more than two countries people have lived in. We argue that these data are very valuable because this type of information is not available in traditional demographic sources which record country-to-country migration flows independent of each other. We show that migration clusters of country triads cannot be identified using information about bilateral flows alone. To demonstrate the additional insights that can be gained by using data about migration clusters, we first develop a model that tries to predict the prevalence of a given triad using only data about its constituent pairs. We then inspect the groups of three countries which are more or less prominent, compared to what we would expect based on bilateral flows alone. Next, we identify a set of features such as a shared language or colonial ties that explain which triple of country pairs are more or less likely to be clustered when looking at country triples. Then we select and contrast a few cases of clusters that provide some qualitative information about what our data set shows. The type of data that we use is potentially available for a number of social media services. We hope that this first study about migration clusters will stimulate the use of Web data for the development of new theories of international migration that could not be tested appropriately before.
Datasets on object manipulation and interaction: a survey
Venue: arXiv cs.RO 2016
Year: 2016

A dataset is crucial for model learning and evaluation. Choosing the right dataset to use or making a new dataset requires the knowledge of those that are available. In this work, we provide that knowledge, by reviewing twenty datasets that were published in the recent six years and that are directly related to object manipulation. We report on modalities, activities, and annotations for each individual dataset and give our view on its use for object manipulation. We also compare the datasets and summarize them. We conclude with our suggestion on future datasets.
Scalar Solvability of Network Computation Problems and Representable Matroids
Venue: arXiv cs.IT 2016
Year: 2016

We consider the following \textit{network computation problem}. In an acyclic network, there are multiple source nodes, each generating multiple messages, and there are multiple sink nodes, each demanding a function of the source messages. The network coding problem corresponds to the case in which every demand function is equal to some source message, i.e., each sink demands some source message. Connections between network coding problems and matroids have been well studied. In this work, we establish a relation between network computation problems and representable matroids. We show that a network computation problem in which the sinks demand linear functions of source messages admits a scalar linear solution if and only if it is matroidal with respect to a representable matroid whose representation fulfills certain constraints dictated by the network computation problem. Next, we obtain a connection between network computation problems and functional dependency relations (FD-relations) and show that FD-relations can be used to characterize network computation problem with arbitrary (not necessarily linear) function demands as well as nonlinear network codes.
Spatio-Temporal Network Dynamics Framework for Energy-Efficient Ultra-Dense Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

This article investigates the performance of an ultra-dense network (UDN) from an energy-efficiency (EE) standpoint leveraging the interplay between stochastic geometry (SG) and mean-field game (MFG) theory. In this setting, base stations (BSs) (resp. users) are uniformly distributed over a two-dimensional plane as two independent homogeneous Poisson point processes (PPPs), where users associate to their nearest BSs. The goal of every BS is to maximize its own energy efficiency subject to channel uncertainty, random BS location, and interference levels. Due to the coupling in interference, the problem is solved in the mean-field (MF) regime where each BS interacts with the whole BS population via time-varying MF interference. As a main contribution, the asymptotic convergence of MF interference to zero is rigorously proved in a UDN with multiple transmit antennas. It allows us to derive a closed-form EE representation, yielding a tractable EE optimal power control policy. This proposed power control achieves more than 1.5 times higher EE compared to a fixed power baseline.
Approximate Joint Matrix Triangularization
Venue: arXiv cs.NA 2016
Year: 2016

We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition. By assuming the input matrices to be perturbations of noise-free, simultaneously diagonalizable ground-truth matrices, the approximate joint triangularizers are expected to be perturbations of the exact joint triangularizers of the ground-truth matrices. We provide a priori and a posteriori perturbation bounds on the `distance' between an approximate joint triangularizer and its exact counterpart. The a priori bounds are theoretical inequalities that involve functions of the ground-truth matrices and noise matrices, whereas the a posteriori bounds are given in terms of observable quantities that can be computed from the input matrices. From a practical perspective, the problem of finding the best approximate joint triangularizer of a set of noisy matrices amounts to solving a nonconvex optimization problem. We show that, under a condition on the noise level of the input matrices, it is possible to find a good initial triangularizer such that the solution obtained by any local descent-type algorithm has certain global guarantees. Finally, we discuss the application of approximate joint matrix triangularization to canonical tensor decomposition and we derive novel estimation error bounds.
Hypothesis Testing in the High Privacy Limit
Venue: arXiv cs.IT 2016
Year: 2016

Binary hypothesis testing under the Neyman-Pearson formalism is a statistical inference framework for distinguishing data generated by two different source distributions. Privacy restrictions may require the curator of the data or the data respondents themselves to share data with the test only after applying a randomizing privacy mechanism. Using mutual information as the privacy metric and the relative entropy between the two distributions of the output (postrandomization) source classes as the utility metric (motivated by the Chernoff-Stein Lemma), this work focuses on finding an optimal mechanism that maximizes the chosen utility function while ensuring that the mutual information based leakage for both source distributions is bounded. Focusing on the high privacy regime, an Euclidean information-theoretic (E-IT) approximation to the tradeoff problem is presented. It is shown that the solution to the E-IT approximation is independent of the alphabet size and clarifies that a mutual information based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihood.
Text comparison using word vector representations and dimensionality reduction
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (t-SNE) and how it can be implemented using Python. The technique provides a bird's-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.
Intertwined Viral Marketing through Online Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Traditional viral marketing problems aim at selecting a subset of seed users for one single product to maximize its awareness in social networks. However, in real scenarios, multiple products can be promoted in social networks at the same time. At the product level, the relationships among these products can be quite intertwined, e.g., competing, complementary and independent. In this paper, we will study the "interTwined Influence Maximization" (i.e., TIM) problem for one product that we target on in online social networks, where multiple other competing/complementary/independent products are being promoted simultaneously. The TIM problem is very challenging to solve due to (1) few existing models can handle the intertwined diffusion procedure of multiple products concurrently, and (2) optimal seed user selection for the target product may depend on other products' marketing strategies a lot. To address the TIM problem, a unified greedy framework TIER (interTwined Influence EstimatoR) is proposed in this paper. Extensive experiments conducted on four different types of real-world social networks demonstrate that TIER can outperform all the comparison methods with significant advantages in solving the TIM problem.
Representation learning for very short texts using weighted word embedding aggregation
Venue: arXiv cs.IR 2016
Year: 2016

Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.
Spectrum Occupancy Measurement: An Autocorrelation based Scanning Technique using USRP
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a technique for scanning and evaluating the radio spectrum use. This technique determines the average occupancy of a channel over a specific duration. The technique was implemented using Software Defined Radio units and GNU Radio software. The survey was conducted in Grand Forks, North Dakota, over a frequency range of 824 MHz to 5.8 GHz. The results of this technique were compared to those of two existing techniques, energy detection and autocorrelation, that were also implemented. The results show that the proposed technique is more efficient at scanning the radio spectrum than the other two techniques.
Reducing the Energy Cost of Inference via In-sensor Information Processing
Venue: arXiv cs.AR 2016
Year: 2016

There is much interest in incorporating inference capabilities into sensor-rich embedded platforms such as autonomous vehicles, wearables, and others. A central problem in the design of such systems is the need to extract information locally from sensed data on a severely limited energy budget. This necessitates the design of energy-efficient sensory embedded system. A typical sensory embedded system enforces a physical separation between sensing and computational subsystems - a separation mandated by the differing requirements of the sensing and computational functions. As a consequence, the energy consumption in such systems tends to be dominated by the energy consumed in transferring data over the sensor-processor interface (communication energy) and the energy consumed in processing the data in digital processor (computational energy). In this article, we propose an in-sensor computing architecture which (mostly) eliminates the sensor-processor interface by embedding inference computations in the noisy sensor fabric in analog and retraining the hyperparameters in order to compensate for non-ideal computations. The resulting architecture referred to as the Compute Sensor - a sensor that computes in addition to sensing - represents a radical departure from the conventional. We show that a Compute Sensor for image data can be designed by embedding both feature extraction and classification functions in the analog domain in close proximity to the CMOS active pixel sensor (APS) array. Significant gains in energy-efficiency are demonstrated using behavioral and energy models in a commercial semiconductor process technology. In the process, the Compute Sensor creates a unique opportunity to develop machine learning algorithms for information extraction from data on a noisy underlying computational fabric.
Path planning with Inventory-driven Jump-Point-Search
Venue: arXiv cs.AI 2016
Year: 2016

In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in video-games, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present invJPS, an "inventory-driven" pathfinding approach based on the highly successful grid-based Jump-Point-Search (JPS) algorithm. We show, formally and experimentally, that the invJPS preserves JPS's optimality guarantees and its symmetry breaking advantages in inventory-based variants of game maps.
OpenGeoBase: Information Centric Networking meets Spatial Database applications - Extended Version
Venue: arXiv cs.DB 2016
Year: 2016

This paper explores methodologies, advantages and challenges related to the use of Information Centric Networking (ICN) for realizing distributed spatial databases. Our findings show that the ICN functionality perfectly fits database requirements: routing-by-name can be used to dispatch queries and insertions, in-network caching to accelerate queries, and data-centric security to implement secure multi-tenancy. We present an ICN-based distributed spatial database, named OpenGeoBase, and describe its design choices. Thanks to ICN, OpenGeoBase can quickly and efficiently provide information to database users; easily operate in a distributed way, deploying and using many database engines in parallel; secure every piece of content; naturally slice resources, so that several tenants and users can concurrently and independently use the database. We also show how OpenGeoBase can support a real world Intelligent Transport System application, by enabling discovery of geo-referenced public transportation information.
Privacy-driven Access Control in Social Networks by Means of Automatic Semantic Annotation
Venue: arXiv cs.SI 2016
Year: 2016

In online social networks (OSN), users quite usually disclose sensitive information about themselves by publishing messages. At the same time, they are (in many cases) unable to properly manage the access to this sensitive information due to the following issues: i) the rigidness of the access control mechanism implemented by the OSN, and ii) many users lack of technical knowledge about data privacy and access control. To tackle these limitations, in this paper, we propose a dynamic, transparent and privacy-driven access control mechanism for textual messages published in OSNs. The notion of privacy-driven is achieved by analyzing the semantics of the messages to be published and, according to that, assessing the degree of sensitiveness of their contents. For this purpose, the proposed system relies on an automatic semantic annotation mechanism that, by using knowledge bases and linguistic tools, is able to associate a meaning to the information to be published. By means of this annotation, our mechanism automatically detects the information that is sensitive according to the privacy requirements of the publisher of data, with regard to the type of reader that may access such data. Finally, our access control mechanism automatically creates sanitized versions of the users' publications according to the type of reader that accesses them. As a result, our proposal, which can be integrated in already existing social networks, provides an automatic, seamless and content-driven protection of user publications, which are coherent with her privacy requirements and the type of readers that access them. Complementary to the system design, we also discuss the feasibility of the system by illustrating it through a real example and evaluate its accuracy and effectiveness over standard approaches.
2-tape 1-way Quantum Finite State Automata
Venue: arXiv cs.FL 2016
Year: 2016

1-way quantum finite state automata are reversible in nature, which greatly reduces its accepting property. In fact, the set of languages accepted by 1-way quantum finite automata is a proper subset of regular languages. We introduce 2-tape 1-way quantum finite state automaton (2T1QFA(2))which is a modified version of 1-way 2-head quantum finite state automaton(1QFA(2)). In this paper, we replace the single tape of 1-way 2-head quantum finite state automaton with two tapes. The content of the second tape is determined using a relation defined on input alphabet. The main claims of this paper are as follows: (1)We establish that 2-tape 1-way quantum finite state automaton(2T1QFA(2)) can accept all regular languages (2)A language which cannot be accepted by any multi-head deterministic finite automaton can be accepted by 2-tape 1-way quantum finite state automaton(2T1QFA(2)) .(3) Exploiting the superposition property of quantum automata we show that 2-tape 1-way quantum finite state automaton(2T1QFA(2)) can accept the language L=ww.